Adaptive ∈-ranking on MNK-Landscapes
This work proposes an adaptive isin-ranking method to enhance Pareto based selection, aiming to develop effective many objective evolutionary optimization algorithm. isin-ranking fine grains ranking of solutions after they have been ranked by Pareto dominance, using a randomized sampling procedure c...
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| Vydané v: | 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making s. 104 - 111 |
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| Hlavní autori: | , |
| Médium: | Konferenčný príspevok.. |
| Jazyk: | English Japanese |
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IEEE
01.03.2009
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| ISBN: | 1424427649, 9781424427642 |
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| Abstract | This work proposes an adaptive isin-ranking method to enhance Pareto based selection, aiming to develop effective many objective evolutionary optimization algorithm. isin-ranking fine grains ranking of solutions after they have been ranked by Pareto dominance, using a randomized sampling procedure combined with isin-dominance to favor a good distribution of the samples. In essence, sampled solutions keep their initial rank and solutions located within the virtually expanded dominance regions of the sampled solutions are demoted to an inferior rank. The parameter isin that determines the expanded regions of dominance of the sampled solutions is adapted to each generation so that the number of highest ranked solutions is kept close to a desired number expressed as a fraction of the population size. We enhanced NSGA-II with the proposed method and verify its performance on MNK-Landscapes. Experimented results show that the adaptive method works effectively and that convergence and diversity of the solutions found can improve remarkably on MNK-Landscapes with 3 les M les 10 objectives. |
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| AbstractList | This work proposes an adaptive isin-ranking method to enhance Pareto based selection, aiming to develop effective many objective evolutionary optimization algorithm. isin-ranking fine grains ranking of solutions after they have been ranked by Pareto dominance, using a randomized sampling procedure combined with isin-dominance to favor a good distribution of the samples. In essence, sampled solutions keep their initial rank and solutions located within the virtually expanded dominance regions of the sampled solutions are demoted to an inferior rank. The parameter isin that determines the expanded regions of dominance of the sampled solutions is adapted to each generation so that the number of highest ranked solutions is kept close to a desired number expressed as a fraction of the population size. We enhanced NSGA-II with the proposed method and verify its performance on MNK-Landscapes. Experimented results show that the adaptive method works effectively and that convergence and diversity of the solutions found can improve remarkably on MNK-Landscapes with 3 les M les 10 objectives. |
| Author | Aguirre, H. Tanaka, K. |
| Author_xml | – sequence: 1 givenname: H. surname: Aguirre fullname: Aguirre, H. organization: Fiber-Nanotech Young Researcher Empowerment Program |, Shinshu Univ., Nagano – sequence: 2 givenname: K. surname: Tanaka fullname: Tanaka, K. organization: Fac. of Eng., Shinshu Univ., Nagano |
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| Snippet | This work proposes an adaptive isin-ranking method to enhance Pareto based selection, aiming to develop effective many objective evolutionary optimization... |
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| SubjectTerms | Pareto optimization Sampling methods |
| Title | Adaptive ∈-ranking on MNK-Landscapes |
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